Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 4
1235-1239,1276

T-STAM: end-to-end action recognition model based on two-stream network with spatio-temporal attention mechanism

Shi Xiangbin1,2
Li Yiying1
Liu Fang2
Dai Qin3
1. College of Information, Liaoning University, Shenyang 110036, China
2. College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
3. College of Information, Shenyang Institute of Engineering, Shenyang 110136, China
DOI: 10.19734/j.issn.1001-3695.2020.02.0077

Abstract

Aiming at the problems that the action recognition methods based on two-stream ignores the inter-relationship between feature channels, and has large amount of redundant spatio-temporal information, this paper proposed an end-to-end action recognition model based on two-stream network with spatio-temporal attention mechanism(T-STAM), which realized the full utilization of the key spatio-temporal information in the video. Firstly, this paper introduced the channel attention mechanism to the two-stream basic network, and calibrated the channel information by modeling the dependencies between feature channels to improve the ability of future expression. Secondly, it proposed a CNN-based temporal attention model to learn the attention score of each frame with fewer parameters, which could focus on the frames with significant amplitude of motion. At the same time, it proposed a multi-spatial attention model, which calculated the attention score of each position in frame from different angles to extract motion saliency areas. Then, it fused temporal and spatial features to further enhance the feature representation of video. Finally, this paper input the fused features into the classification network, and fused the results of each stream according to different weights to obtain the recognition results. The experimental results on HMDB51 and UCF101 dataset show that T-STAM can effectively recognize actions in video.

Foundation Support

国家自然科学基金资助项目(61602320)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.02.0077
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 4
Section: Technology of Graphic & Image
Pages: 1235-1239,1276
Serial Number: 1001-3695(2021)04-053-1235-05

Publish History

[2021-04-05] Printed Article

Cite This Article

石祥滨, 李怡颖, 刘芳, 等. T-STAM:基于双流时空注意力机制的端到端的动作识别模型 [J]. 计算机应用研究, 2021, 38 (4): 1235-1239,1276. (Shi Xiangbin, Li Yiying, Liu Fang, et al. T-STAM: end-to-end action recognition model based on two-stream network with spatio-temporal attention mechanism [J]. Application Research of Computers, 2021, 38 (4): 1235-1239,1276. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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